Focal cortical dysplasia (FCD) is a frequent cause of epilepsy and can be detected using brain magnetic resonance imaging (MRI). One important MRI feature of FCD lesions is the blurring of the gray-white matter boundary (GWB), previously modelled by the gradient strength. However, in the absence of additional FCD descriptors, current gradient-based methods may yield false positives. Moreover, they do not explicitly quantify the level of blur which prevents from using them directly in the process of automated FCD detection. To improve the detection of FCD lesions displaying blur, we develop a novel algorithm called iterating local searches on neighborhood (ILSN). The novelty is that it measures the width of the blurry region rather than the gradient strength. The performance of our method is compared with the gradient magnitude method using precision and recall measures. The experimental results, tested on MRI data of 8 real FCD patients, indicate that our method has higher ability to correctly identify the FCD blurring than the gradient method.